R0027/2026-03-26/Q002/SRC01
Vatsal et al. — Linguistic features affecting prompt effectiveness
Source
| Field |
Value |
| Title |
Multilingual Prompt Engineering in Large Language Models: A Survey Across NLP Tasks |
| Publisher |
arXiv |
| Author(s) |
Shubham Vatsal, Harsh Dubey, Aditi Singh |
| Date |
2025-05-16 |
| URL |
https://arxiv.org/abs/2505.11665 |
| Type |
Survey / review paper |
Summary
| Dimension |
Rating |
| Reliability |
Medium-High |
| Relevance |
High |
| Bias: Missing data |
Some concerns |
| Bias: Measurement |
N/A |
| Bias: Selective reporting |
Some concerns |
| Bias: Randomization |
N/A — not an RCT |
| Bias: Protocol deviation |
N/A — not an RCT |
| Bias: COI/Funding |
Low risk |
Rationale
| Dimension |
Rationale |
| Reliability |
Comprehensive survey but preprint. Covers linguistic features systematically. |
| Relevance |
Directly discusses how morphology, syntax, and lexico-semantic features affect prompt performance. |
| Bias flags |
Some concerns about missing data — limited coverage of tonal language specifics. Some concerns about selective reporting — emphasis on techniques over linguistic analysis. |
| Evidence ID |
Summary |
| SRC01-E01 |
Linguistic features (morphology, syntax, lexico-semantics) significantly influence prompt effectiveness |
| SRC01-E02 |
Japanese requires explicit subject markers; Arabic needs gender-specific context |